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J. Nightingale et al.: Optimised Transmission of H.264 Scalable Video Streams over Multiple Paths in Mobile Networks 2161
Optimised Transmission of H.264 Scalable Video Streams
over Multiple Paths in Mobile Networks
James Nightingale, Student Member , Qi Wang, Member , and Christos Grecos, Senior Member , IEEE
Abstract — Consumer demand for portable wireless
devices such as smartphones or tablets capable of receiving
high quality video content has risen sharply in recent years.
This paper considers the real-time delivery of streamed H.264
Scalable Video Coding (SVC) content to users of such devices
within mobile networks (e.g. situations where they are part of
a group moving together on a bus or a train). We propose a
novel scheme for the multipath delivery of H.264 SVC content
to users in multihomed mobile networks .By implementing our
scheme on a realistic testbed, we show that it offers a
significant improvement in received video quality over
previously proposed alternative schemes1.
Index Terms — H.264 SVC, Mobile Networks, Multihomed,Multipath Streaming.
I. INTRODUCTION
The use of a wide range of sophisticated personal wireless
devices (laptop, netbook, smartphone, tablets etc.); is
becoming commonplace in society. Users of such equipment
will expect to be able to make full use of a device’s
capabilities in everyday situations including when travelling
on public transport. There are numerous technical challenges
associated with streaming media content to nomadic users in
public transport situations. These include mobility
management, the low available bandwidth on some public
networks and the lack of universal coverage by any individualnetwork.
An emerging wireless networking paradigm known as
Mobile Networks [1] has been developed to address the
mobility requirements of groups of users (or network devices)
travelling together in unison. Mobile devices, when acting as
nodes in a mobile network, no longer directly connect to the
user’s ISP but rather to a local device within the mobile
network that handles mobility on behalf of all nodes.
Multihomed mobile networks are those in which multiple
heterogeneous access paths to the network can be accessed
and used simultaneously.
Scarcity of available bandwidth on public access networks
may mean that there is insufficient bandwidth on any singlenetwork path to ensure delivery of a media stream from server
to client. The recent introduction of the H.264 Scalable Video
Coding [2] extension to the H.264 Advanced Video Coding
Standard (AVC) [3] provides the ability to adapt video
streams in response to varying network conditions.
1 James Nightingale, Qi Wang and Christos Grecos are with the Audio
Visual Communications and Networks Group, School of Computing,
University of the West of the Scotland, Paisley, United Kingdom (e-mail:
Another way in which bandwidth limitations can beovercome is by using the aggregated bandwidth of all
available network paths from streaming server to client in
order to maximize the throughput. A number of schemes
have been proposed to make best use of this aggregated
bandwidth to ensure the delivery of MPEG2 and MPEG4
streams to client nodes which are either static or nomadic
but not part of a mobile network. Any such scheme must
take account of both available network path conditions
and the characteristics of the media stream itself when
deciding which path(s) to use for streaming and should
ensure that the simultaneous use of multiple paths does
not lead to a higher incidence of out-of-sequence packet
reception at the client.In this work we apply typical multipath streaming
algorithms to H.264 SVC and practically implement them
on a testbed offering a realistic multihomed mobile
networks environment. We empirically evaluate them and
propose an optimised multipath streaming algorithm for
use in this previously unconsidered environment. As
previous work on multipath streaming schemes has not
focused on this environment, some practical factors that
affect the performance of multipath streaming algorithms,
if applied to multihomed mobile networks, have not been
considered. The most significant factors are the non-
negligible addition of tunnelling overheads in mobile
networks and the path switching delay in multihomedmobile networks.
Our technical contributions are multi-fold and are
outlined as follows. Firstly, in our implementation, packet
priority weighting schemes designed for AVC in existing
work have been extended to include the rich scalability
vectors of SVC and to utilise the by far greater degree of
granularity offered by SVC over AVC. Secondly we
mitigate the previously unconsidered tunneling and path
switching overheads encountered in mobile networks.
Lastly, we seek to reduce path switching frequency (and
associated delay) by both scheduling at an RTP packet
level; rather than the previously proposed IP level; andtrading off bandwidth aggregation in favour of reduced
path switching frequency.
The rest of the paper is organized as follows. Related
work is reviewed in Section II. Section III describes our
proposed scheme, while our testbed and implementation is
presented in Section IV. Section V presents the results of
our empirical comparison of multipath scheduling
algorithms and Section IV concludes the paper.
Contributed Paper Manuscript received 10/15/10Current version published 12/23/10
Electronic version published 12/30/10. 0098 3063/10/$20.00 © 2010 IEEE
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IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, November 20102162
II. R ELATED WORK
This work brings together aspects of research in both the
computer networking community and the real-time video
processing community.
A. Mobile Networks
Fig. 1. A typical multihomed mobile networks topology.
The Network Mobility (NEMO) [4] protocol, a further
development of MIPv6 [5], allows groups of users travelling
together to connect to a local device called a Mobile Router
(MR) that handles the mobility management requirements of
all of its attached mobile network nodes (MNN). Fig. 1 shows
a simplified topology of a multihomed mobile network.
Mobility is handled by two NEMO mobility agents, one of
which is the Home Agent (HA) residing in the MR’s home
network and the other is at the MR itself . Any data packets
destined for a node in the mobile network are routed via the
HA where they are encapsulated for tunnelling to the MR.
Packets are then transmitted over a bi-directional tunnel
between HA and MR. At the MR the encapsulation header is
removed and the packets are forwarded to the MNN. The
tunnelling process from HA to MR adds an additional network
overhead to each IP packet transmitted and as the traffic from
all network nodes must pass through the link from the MR to
the radio access point, this link can become the bottleneck on
the transmission path. In multihomed mobile networks the
mobile router has multiple network interfaces, each of which
is connected to a different radio access network. These access
networks, may employ a variety of heterogeneous radio
technologies or simply provide homogeneous connections to
different service providers. The MR is able to makesimultaneous use of all attached access networks, and thus
circumvent the single link bottleneck.
We have previously addressed the issue of path selection in
multihomed mobile networks by proposing a scheme [6], [7]
to provide an always best connected path in such networks. In
that scheme, we provided a general-policy driven mechanism
that exploits current path condition metrics and application
specific rules to determine the current best path for an
application flow. The path is changed as required at the
application flow level per application. In this paper, we
greatly extend our previous work by designing and
implementing a novel RTP packet level scheduling and
switching scheme for SVC streaming.
B. Scalable Video Coding
H.264 SVC allows the encoding of video sequences as a
number of sub-streams. In SVC a stream consists of an AVC
compliant base layer, providing a minimum quality of video,
and a number of enhancement layers, which improve thequality of the received stream. The three-dimensional
scalability of SVC (spatial, temporal and quality), can be used
for network or terminal adaptation of streams. To conserve
network resources, a sender only transmits those layers that a
client node is capable of processing. If there is insufficient
bandwidth to deliver the entire stream network adaptation may
employed. This will drop higher enhancement layers, reducing
the bandwidth requirement and thus ensuring delivery of the
base layer and lower enhancement layers. Providing the user
with an acceptable quality of video and making efficient use
of available bandwidth.
A number of schemes have been proposed for the adaptionof SVC streams in response to varying network conditions [8],
[9]. In [8] an Adaption Decision Taking Engine (ADTE) is
placed at the streaming server. It makes real-time stream
adaption decisions based on client capability and network path
data. This data is contained in MPEG-21 DIA [10] messages
sent from the client. The scheme proposed in [9] considers
both network conditions and device energy consumption.
Neither of these has considered the delivery of SVC in
multihomed mobile networks. While work in the IETF to
provide a standard for the delivery of SVC over RTP [11] is
nearing completion, no freely available streaming servers or
playback clients for SVC are currently available. In the
absence of such standards and tools, we explore and further develop the Scalable Video Evaluation Framework [12]
(SVEF) for the empirical evaluation of real- time SVC
streaming.
C. Multipath Streaming Algorithms
A number of Quality of Service (QoS) related schemes have
been proposed to improve delay sensitive media streaming
over IP based networks, some of which have considered the
use of the aggregated available bandwidth of all network paths
from streamer to client. Media-aware schemes such as those
proposed by Chebrolou and Rao [13], [14] and Jurca and
Frossard [15]; take account of the characteristics of both the
media stream and current conditions on available network
paths when deciding how to distribute a video stream across
multiple paths. Of these schemes, only [15] considers a
scalable video format; however, [15] is a simple generic
format rather than the sophisticated three dimensional
scalability of SVC. These multipath streaming algorithms
make path selection and scheduling decisions on a per packet
basis at the IP level and in the case of [15] drops packets that
cannot be successfully scheduled. This differs significantly
from schemes such as [8] where adaptation is at a per SVC
layer granularity. Multipath scheduling and scheduling can
lead to out-of-sequence delivery of packets at the client, which
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J. Nightingale et al.: Optimised Transmission of H.264 Scalable Video Streams over Multiple Paths in Mobile Networks 2163
then requires a larger input buffer; this can be a limiting factor
for resource-constrained mobile devices. In Earliest Delivery
First (EDF) [13] and Earliest Delivery Path First (EDPF) [14]
algorithms, the network metrics of available bandwidth and
delay on each path and the size of each packet are used to
estimate the arrival time at the client. A packet is always sent
on the path offering the earliest arrival time, thus reducing
out-of-sequence delivery. The heuristic load balancing
algorithm (LBA) from [15] also performs stream adaptation inresponse to changing network path states by only transmitting
those packets that are estimated to arrive at the client in time
to be of use in the decoding process. Additionally, LBA
conserves bandwidth by dropping packets that cannot be
decoded because they rely (for decoding) on a previous packet
that has already been dropped. A packet prioritisation scheme
in LBA gives a higher weighting to I frames over B and P
frames and also to base layer packets over enhancement layer
packets. The LBA scheduler sorts packets according to
priority weighting, and sacrifices lower priority packets to
ensure the delivery of those with a higher priority.
Placement of the scheduling mechanism in LBA is at the
streaming server equipped with multiple network interfaces,each of which provides a completely independent path to the
client. In EDPF, the mechanism is placed at the HA. Both
approaches have limitations. In the case of LBA it is more
likely that, in a realistic network setting, the point of
divergence of paths will be at a router rather that at the server
itself. With EDPF the issue is of placing a computationally
intensive mechanism on the HA, which is a router rather than
a sever and has already been occupied with mobility
management tasks. Moreover, if a bi-directional streaming
process is considered, the scheduling burden would fall on the
resource constrained MR when the MNN is acting as the
streamer.
EDPF assumes a stable negotiated bandwidth for theduration of a streaming session, while LBA and the scheme in
[8] consider dynamic path conditions.
III. PROPOSED ALGORITHM
We propose a path selection and packet scheduling
algorithm for use in multihomed mobile networks in which we
take account of the previously unconsidered network
overheads associated with this environment.
A. Factors Influencing Path Choice
Each of the algorithms discussed in section II considers a
different set of factors when making a path selection or packet
scheduling decision. EDPF only considers the size of a media
packet together with available bandwidth and delay but ignore
packet dependencies and the unequal importance of packets in
a video stream. While LBA does consider these additional
factors and also queuing times at intermediate routing nodes,
it was targeted at wired networks and did not address mobility
issues. The first additional factor that we consider is the
mobility-related networking overhead added to each IP packet
transmitted by the streamer. If the size of an RTP packet
exceeds the Ethernet maximum transmission unit (MTU) size
of 1500 bytes it will be fragmented into several IP packets,
each of which will have its own mobility related network
overhead added.
Fig. 2. RTP packet sizes in the 30 fps of the Soccer sequence. The majority
of RTP packets are larger than the Ethernet MTU. Almost 5% are more
than ten times the MTU size.
The size of the additional overhead added to each packet is
dependent on the level of nesting within a mobile network.
Each packet is tunnelled from the HA to the MR and has an
encapsulation overhead added to the packet’s overall
transmitted size. In our experiments, 58 % of packets in the
Bus sequence at 30 fps were greater than the MTU, as can be
seen in Fig. 2, and this was higher at 70% for the 30 fps
Soccer sequence. The second additional factor that we
consider is the path switching delay in multihomed mobile
networks. In previous work [6], [7] we proposed a path
switching mechanism to support our Always Best Connected
path approach. In this work we have further implemented this
switching mechanism as a path control module at the
streaming server and a client module at the HA where path
switching takes place. As a precursor to our main streaming
experiments, we measured the delay introduced by each path
switching operation as shown in Fig. 3. It was found that the
average switching delay in our realistic mobile networks
testbed is 137ms.
Fig. 3. Delay introduced by path switching using the Bus sequence at
30fps.
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J. Nightingale et al.: Optimised Transmission of H.264 Scalable Video Streams over Multiple Paths in Mobile Networks 2165
By extending this scheme to all three dimensions such that
weighting = ( .Lid)+( .Tid)+Qid, we provide a scheme that
reflects the full granularity of SVC.
In this work, we applied only a small number of the
available weightings rather than deploying the full granularity
described above. We did this in order to provide the ability to
map our weightings to the scheme used in [15] that is reliant
on I, P and B frame types rather than the SVC scalability data.
We are therefore able to provide a fair comparison with
representative algorithms from literature and evaluate effects
of the other novel components in our architecture. We
performed this mapping in our pre-processing module.
It should also be noted that the default JSVM encoder [16]
output order for SVC streams already offers a degree of
prioritisation of NAL units by sending those pictures (within a
GOP), which are required for prediction by others in the GOP
earliest to try and ensure their delivery. This is shown in Fig.
5.In LBA [15] all ancestors of a packet are identified. If any
packet upon which the current packet relies for decoding has
not already been scheduled, LBA attempts to do so. If any
ancestor is cannot be scheduled, the current packet is dropped.The exact method of determining a packets ancestors in real-
time is not discussed in [15].
Our practical implementation limits the ancestor checking
function to within the current window of knowledge (read
ahead window) of the streamer. To identify a packet’s
ancestors, we make use of SVC scalability information, frame
number and GOP size data. The frame number and scalability
information of any NAL unit contained in dropped packet
within the current window (typically 1 or 2 GOP’s in length)
is stored in memory. By comparing the frame number, Lid,
Tid and Qid of the current packet to those of failed packets in
the current window, we can establish if a packet’s ancestorshave been scheduled without the need for the expensive
recursive searching method employed in [15]. As the read
ahead window is small, the data stored for each packet is only
5 bytes and only data for packets dropped in the current
window is stored; the memory overhead in our scheme is
minimal, making it suitable for resource-constrained devices.
Fig. 5. Inputs considered by each algorithm and possible scheduling
outcomes that can be made.
Fig. 6. Path Monitoring & Control.
Since current path measurement tools generally need a
number of round trip times to accurately estimate path
conditions and the authors of EDPF and LBA have assumed
the instant availability of current path metrics, we designed
and implemented a path control mechanism, which makeschanges to path conditions within the core network and
reports any changes (in less than 10ms) to the streaming
server. This is similar to the virtual choke point in [17]. Fig 6
provides a diagram of the signalling involved in this path
monitoring and control mechanism.
When a path change is required, the path switching control
module at the streamer signals the new path number to the
client module on the HA. Where the change is implemented
and acknowledged. The controller then updates the scheduler
with the time taken for the path switching. This allows the
time at which a path next becomes free to be accurately
estimated when calculating the arrival time of the next packet.
Signalling for this is shown in Fig. 7. A diagrammatic
representation of our OPSSA algorithm is given in Fig. 8.
Fig. 7. Path Switching Mechanism
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Fig. 8. Optimized scheduling algorithm.
IV. IMPLEMENTATION
Fig. 9. Topology of our multihomed mobile networks testbed.
We provide a practical Linux user space implementation of
three path selection and scheduling algorithms (EDPF, LBA
and the proposed OPSSA) on a realistic multihomed mobilenetworks testbed. Our testbed, the topology of which is shown
in Fig. 9, consists of standard PCs running Ubuntu Linux for
the video streaming server, the mobility-management home
agent, the core routers, the mobile network router and mobile
network clients in our testbed. Two paths are provided
between the video streaming server and the multihomed
mobile network. Each path consists of a 100Mbps Ethernet
wired link incorporating a core router running wide-area
network emulation and path monitoring modules and an IEEE
802.11g wireless link offered by a modified Linksys
WRT54GL wireless router. All PCs used in the testbed have
3.4 GHz Pentium 4 processors with mobility agents and core
routers having 1 GB of RAM and the end nodes (streaming
server and mobile client) having 512 MB of RAM. Mobility
management is provided by NEMO running at both the home
agent and the mobile router.
Fig. 10. Testbed path switching and monitoring overview.
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J. Nightingale et al.: Optimised Transmission of H.264 Scalable Video Streams over Multiple Paths in Mobile Networks 2167
Fig. 10 gives an overview of the design of our testbed. We
incorporate mechanisms to provide path switching and path
monitoring functions which, when combined with our
scheduling implementation, provide an application-specific
(SVC streaming) instance of the Network Selection Algorithm
[6] (NSA) or always best connected service [7] introduced in
our previous work.
SVEF [12] is an open source testing and evaluation tool for
single path IPv4 transmission of stored SVC content. As nextgeneration networks use the IPv6 version of the Internet
Protocol, we have rewritten the network interface of SVEF to
support both IPv4 and IPv6 address families and the MIPv6
based mobility management software used in mobile
networks. The SVEF trace file is extended by rewriting the
existing pre-processing module to calculate the relative
decoding deadline (in relation to the first NAL unit in a
stream) and priority weighting of each NAL unit. The pre-
processor permits the easy sorting of the trace file to test a
number of algorithms and streaming scenarios.
V. EVALUATION
All three algorithms (EDPF, LBA and the proposed
OSPSA) that have been implemented on our mobile networks
testbed are empirically compared. Publicly available video
sequences were encoded using the JSVM reference software
and the performance of each algorithm compared in terms of
packet delivery statistics and statistical video quality metrics.
Versions of the Soccer sequence with 4CIF (704x576)
resolution were used at frame rates of 30 and 60 fps, together
with versions of the Bus sequence with QCIF (176x144)
resolution and frame rates of 15 and 30 fps. Each was
encoded with a base layer and two MGS scalability layers.
We implemented a comprehensive logging scheme at both
the streamer and client. The streamer recorded packet size,
identity, SVC scalability data, mobility overhead and the
scheduling decision for each packet. Each path switching and
the delay introduced was also recorded. At the client the
arrival time relative to the first packet in the stream is
recorded.
To understand the effect on performance of added mobility
overheads, we compared two versions of our optimised
algorithm, one which mitigated the additional overheads and
one which used the payload size only when calculating
expected the arrival time of a packet. We used both sequences
across the full range of frame rates. The mobility overhead
added to the Soccer sequence at 30 fps increased the size of
the stream by 4.83% the added overhead will be even more
significant in nested mobile networks with hierarchical mobilerouters. When the overheads were not taken into account, the
number of packets arriving at the client that were unusable at
the client (either due to arriving after their decoding deadline
or because an ancestor had arrived after its decoding deadline)
increased on average by 6.1%. The Bus sequence, which has
fewer large packets, performed better than the Soccer
sequence. The number of base layer packets failing to arrive
on time also increased (from 0.05% to 0.8 % for the Soccer
sequence) and received video quality was reduced. The PSNR
of the Soccer sequence was reduced by 0.36 dB and the Bus
sequence by 0.14 dB. We, therefore, have shown that the
effects on video quality of added mobility overheads in mobile
networks are significant.
Fig. 11. Path switching frequency (data collected at the streamer).
Path switching frequency is influenced by a number of
factors, apart from the algorithm used. These include the
relative difference between paths in terms of available bandwidth and delay and the size of the RTP packets being
scheduled as larger packets have longer transmission times. In
our experiments, we found that packet size influenced path
switching frequency, effective bandwidth aggregation rate and
was the largest single factor (when translated to transmission
time) even on paths with higher available bandwidth and low
delay. In our algorithm, we trade off effective bandwidth
aggregation and reduced path switching frequency. This
strategy, as can be seen in Fig 11, is more effective when
paths characteristics are unequal. OPSSA outperforms all of
the others, including a path switching compensated version of
LBA that we implemented for comparative purposes andconfigured with a 137ms path switching compensation value.
The cumulative effect of unmitigated path switching delays
can be clearly seen in Fig. 12, where packets delivered via
EDPF and LBA implementations, which do not consider the
switching cost, very quickly begin to deliver packets later than
their decoding deadline and thus rendering them useless in the
decoding process.
Fig. 12. Comparison of packet arrival times at client for EDPF, LBA and
OPPSA using a 30 fps Bus sequence.
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Fig. 13. Packets arriving at the client, those that are within their decode
window and those which are useable in the decode process.
The results clearly demonstrate OPSSA delivers themajority of packets within the decoding deadline. The cyclic
nature of the decoding deadline on the graph results from the
way in which packets are sorted by priority weighting and
then decoding deadline on a per read ahead window basis. It
should be noted that only those packets that arrived at the
client for each frame are included in the graph. Fewer packets
are delivered by OPSSA than either EDPF or LBA due to the
dropping at the streamer of packets that were estimated not to
arrive on time.
From Fig 13 it can be observed that OPSSA provides the
highest number of useable packets to the client while also
sending the lowest number of packets. This effect was more
pronounced on high differential paths and on the Soccer
sequence where the packet size distribution in the stream is
less equal than with the Bus sequence. Even if a packet arrives
on time it can only be used if packets that it depends on also
arrived on time.
The received video quality, when measured using the
statistical Peak Signal to Noise Ratio (PSNR) metric is higher
for OPSSA than for the other schemes. EDPF and LBA do not
perform satisfactorily as they do not consider the additional
overheads and switching costs. Path switching compensated
LBA performs better but still has a higher path switching
frequency and does not consider the mobility overheads.
Although it provides an acceptable quality of received video,it does not perform as well as OPSSA.
The results of our received video quality measurements are
shown in Fig. 14. The PSNR of both LBA and EDPF quickly
fall below acceptable limits while OPSSA and path switching
compensated LBA perform significantly better, confirming
our hypothesis that path switching overhead is a significant
limiting factor that effects multipath streaming in multihomed
mobile networks. One of the limitations of the current version
of the JSVM decoder is its inability to correctly deal with
Fig. 14. Video quality comparison measured using PSNR
packets that have unmet dependencies. We, therefore use the
SVEF framefiller mechanism to firstly generate a filtered copy
of the video sequence containing only those packets that
correctly arrived at the client on time to be of use in the
decoding process and had all ancestor packet available at the
decoder. The simple SVEF frame filler routine is then applied
to conceal missing parts of the sequence. This reconstructed
video sequence is compared to the original sequence using the
JSVM reference software PSNR comparison tool.
VI. CONCLUSIONS
In this work, we have introduce a scheme for the delivery
of H.264 SVC streams across multiple paths in multihomed
mobile networks. We have demonstrated that mobility
overheads and path switching costs are significant factors thatmust be considered when distributing a stream across multiple
paths in this environment. Furthermore, we have shown that
our algorithm (OPSSA) outperforms representative algorithms
from literature (in terms of PSNR) when implemented in this
context. Our experiments were performed on a testbed
environment with realistic switching costs of; on average
137ms.
By trading off bandwidth aggregation against a reduced
level of path switching, our scheme; provides a higher quality
video stream to the client. Improvements range from 0.4 to 1.0
dB (dependant on sequence and encoding) for equal paths, to
in excess of 2.3dB for paths where one has a substantiallyhigher capacity than the other.
Our work has substantially extended previous
representative algorithms for use with H.264 SVC streams in
multihomed mobile networks. We have provided both an SVC
packet prioritisation scheme suitable for use with multipath
streaming algorithms and a low cost means of determining, if
a packet’s ancestors have been scheduled in real time. The
proposed algorithm OPSSA, is suitable for use on resource-
constrained devices.
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BIOGRAPHIES
James Nightingale (S’09) received the BSc degree in
Network Computing from Edinburgh Napier
University, UK and the BSc (Hons) degree in Computer
Networks from the University of the West of Scotland,
UK, where he currently a Ph.D. student. His research
interests include mobile networks, multihoming and
video streaming techniques.
Qi Wang (S’02-M’06) Dr Qi Wang is a Lecturer in
Computer Networking with the University of the West
of Scotland (UWS), UK. Previously, he was a
Postdoctoral Research Fellow with the University of
Strathclyde, UK, and a Telecommunications engineer
with the State Grid Corporation of China. He received
his PhD in Mobile Networking from the University of
Plymouth, UK, and his BEng and MEng degrees from
Dalian Maritime University, China. Recently, he has been involved in the
European Union FP6 MULTINET project and the UK EPSRC DIAS
project. His research interests include Internet Protocol networks and
applications, diverse wireless networks, mobility management,
multihoming support and intelligent network selection, and cross-layer design. He is a member of IEEE, and on the technical programme
committees of a number of international conferences.
Christos Grecos (M’01-SM’06) Prof Christos Grecos
is a Professor in Visual Communications Standards,
and Head of School of Computing, the University of
the West of Scotland (UWS), UK. He leads the Audio-
Visual Communications and Networks Research Group
(AVCN) with UWS, and his research interests include
image/video compression standards, image/video
processing and analysis, image/video networking and
computer vision. He has published numerous research papers in top-tier
international publications including a number of IEEE transactions on these
topics. He is on the editorial board or served as guest editor for numerousinternational journals, and he has been invited to give talks in various
international conferences. He was the Principal Investigator for several
national or international projects funded by UK EPSRC or EU. He received
his PhD degree in Image/Video Coding Algorithms from the University of
Glamorgan, UK. He is a Senior Member of IEEE.